666 responses from 222 respondents to 3 items.
One of 4 tables from the same source; this one is the professional_knowledge table. The others are listed under Related tables.
| Description | Self-rated professional knowledge, 3 items, 1-7, Chinese clinicians. 222 respondents x 3 items = 666 responses. |
|---|---|
| Reference | Cao, M., Luo, J., & Zhang, Q. (2026). The Double-Edged Effect of Interaction in Clinical Decision Support Systems: Cognitive Load, Perceived Autonomy, and Physicians' Intention to Use [Data set]. figshare. https://doi.org/10.6084/m9.figshare.33204345.v2 |
| Licence | CC BY 4.0 |
| Source data | https://doi.org/10.6084/m9.figshare.33204345.v2 |
| Responses | 666 |
|---|---|
| Respondents | 222 |
| Items | 3 |
| Response categories | 7 |
| Responses per respondent | 3 |
| Responses per item | 222 |
| Density | 1 |
| Longitudinal | FALSE |
| sample | Workplace |
|---|---|
| measurement tool | Survey/questionnaire |
| item format | Likert Scale/selected response |
| primary language(s) | chi |
This codebook is reconstructed by the IRW from the IRW data standard and the script that built it, cao_2026_cdss.py. It is our best reading, not the source’s own codebook, and it may contain mistakes. Where the two disagree, the source data are the authority. If you find an error, please tell us.
| Column | What it is | Source column |
|---|---|---|
| IRW standard columns | ||
id | IRW standard Identifier for the focal unit being measured — typically a person, but sometimes another entity (e.g., a word in a lexical task). | made in the build script |
item | IRW standard Item identifier. | made in the build script |
resp | IRW standard Response value. | made in the build script |
treat | IRW standard Treatment group assignment in experimental studies. | made in the build script |
| Respondent covariates | ||
cov_age_band | Covariate | Age |
cov_education | Covariate | Education |
cov_gender | Covariate | Gender |
cov_professional_rank | Covariate | Professional rank |
What item codes and covariate values stand for is in the codebook released with the source data.
Or load it directly in R or Python (both need a free Redivis account; the first call opens a browser to sign in):
# R
install.packages("remotes")
remotes::install_github("itemresponsewarehouse/Rpkg")
library(irw)
df <- irw_fetch("cao_2026_cdss_professional_knowledge")
# Python
pip install irw
import irw
df = irw.fetch("cao_2026_cdss_professional_knowledge")
@misc{https://doi.org/10.6084/m9.figshare.33204345,
doi = {10.6084/M9.FIGSHARE.33204345},
url = {https://figshare.com/articles/dataset/The_Double-Edged_Effect_of_Interaction_in_Clinical_Decision_Support_Systems_Cognitive_Load_Perceived_Autonomy_and_Physicians_Intention_to_Use/33204345},
author = {cao, mukun and Luo, Jian and Zhang, Qiwei and Li, Bing},
keywords = {Digital health, Health informatics and information systems, Health systems},
title = {The Double-Edged Effect of Interaction in Clinical Decision Support Systems: Cognitive Load, Perceived Autonomy, and Physicians’ Intention to Use},
publisher = {figshare},
year = {2026},
copyright = {Creative Commons Attribution 4.0 International}
}
| IRW version | v549 |
|---|---|
| Redivis dataset | item_response_warehouse_4 v13.0 |
| Redivis dataset DOI | 10.57761/w8zx-td92 |
| Manifest pin for this IRW version | v13.0 |
| Metadata source | irw_meta v39.0 |